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New genetic insights into immunotherapy outcomes in gastric cancer via single-cell RNA sequencing and random forest model.
Yu, Dajun; Yang, Jie; Wang, BinBin; Li, Zhixiang; Wang, Kai; Li, Jing; Zhu, Chao.
Affiliation
  • Yu D; Jinan University, Guangzhou, Guangdong, China. 012003044@bbmc.edu.cn.
  • Yang J; Department of Radiation Oncology, The Second Clinical Medical College (Shenzhen People's Hospital) of Jinan University, Shenzhen, Guangdong, China. 012003044@bbmc.edu.cn.
  • Wang B; Department of Surgical Oncology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, 233000, People's Republic of China. 012003044@bbmc.edu.cn.
  • Li Z; Department of Surgical Oncology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, 233000, People's Republic of China.
  • Wang K; Department of Surgical Oncology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, 233000, People's Republic of China.
  • Li J; Department of Surgical Oncology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, 233000, People's Republic of China.
  • Zhu C; Department of Surgical Oncology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, 233000, People's Republic of China.
Cancer Immunol Immunother ; 73(6): 112, 2024 May 02.
Article in En | MEDLINE | ID: mdl-38693422
ABSTRACT

OBJECTIVE:

The high mortality rate of gastric cancer, traditionally managed through surgery, underscores the urgent need for advanced therapeutic strategies. Despite advancements in treatment modalities, outcomes remain suboptimal, necessitating the identification of novel biomarkers to predict sensitivity to immunotherapy. This study focuses on utilizing single-cell sequencing for gene identification and developing a random forest model to predict immunotherapy sensitivity in gastric cancer patients.

METHODS:

Differentially expressed genes were identified using single-cell RNA sequencing (scRNA-seq) and gene set enrichment analysis (GESA). A random forest model was constructed based on these genes, and its effectiveness was validated through prognostic analysis. Further, analyses of immune cell infiltration, immune checkpoints, and the random forest model provided deeper insights.

RESULTS:

High METTL1 expression was found to correlate with improved survival rates in gastric cancer patients (P = 0.042), and the random forest model, based on METTL1 and associated prognostic genes, achieved a significant predictive performance (AUC = 0.863). It showed associations with various immune cell types and negative correlations with CTLA4 and PDCD1 immune checkpoints. Experiments in vitro and in vivo demonstrated that METTL1 enhances gastric cancer cell activity by suppressing T cell proliferation and upregulating CTLA4 and PDCD1.

CONCLUSION:

The random forest model, based on scRNA-seq, shows high predictive value for survival and immunotherapy sensitivity in gastric cancer patients. This study underscores the potential of METTL1 as a biomarker in enhancing the efficacy of gastric cancer immunotherapy.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Stomach Neoplasms / Single-Cell Analysis / Immunotherapy Limits: Animals / Female / Humans / Male Language: En Journal: Cancer Immunol Immunother Journal subject: ALERGIA E IMUNOLOGIA / NEOPLASIAS / TERAPEUTICA Year: 2024 Document type: Article Affiliation country: China

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Stomach Neoplasms / Single-Cell Analysis / Immunotherapy Limits: Animals / Female / Humans / Male Language: En Journal: Cancer Immunol Immunother Journal subject: ALERGIA E IMUNOLOGIA / NEOPLASIAS / TERAPEUTICA Year: 2024 Document type: Article Affiliation country: China